This paper illustrates the technologies of user next intent prediction with a concept knowledge graph. The system has been deployed on the Web at Alipay, serving more than 100 million daily active users. To explicitly characterize user intent, we propose AlipayKG, which is an offline concept knowledge graph in the Life-Service domain modeling the historical behaviors of users, the rich content interacted by users and the relations between them. We further introduce a Transformer-based model which integrates expert rules from the knowledge graph to infer the online user's next intent. Experimental results demonstrate that the proposed system can effectively enhance the performance of the downstream tasks while retaining explainability.

翻译：本文阐述了利用概念知识图谱进行用户后续意图预测的技术。该系统已在支付宝网页端部署，服务于超过1亿日活跃用户。为显式刻画用户意图，我们提出了AlipayKG——一个生活服务领域的离线概念知识图谱，用于建模用户历史行为、用户交互的丰富内容及其间关系。我们进一步引入基于Transformer的模型，该模型融合了知识图谱中的专家规则，以推断在线用户的后续意图。实验结果表明，所提出的系统在保持可解释性的同时，能有效提升下游任务的性能。